23 May 2024
 | 23 May 2024
Status: this preprint is open for discussion.

Detection and Tracking of Carbon Biomes via Integrated Machine Learning

Sweety Mohanty, Lavinia Patara, Daniyal Kazempour, and Peer Kröger

Abstract. In the framework of a changing climate, it is useful to devise methods capable of effectively assessing and monitoring the changing landscape of air-sea CO2 fluxes. In this study, we developed an integrated machine learning tool to objectively classify and track marine carbon biomes under seasonally and interannually changing environmental conditions. The tool was applied to the monthly output of a global ocean biogeochemistry model at 0.25° resolution run under atmospheric forcing for the period 1958–2018. Carbon biomes are defined as regions having consistent relations between surface CO2 fugacity (fCO2) and its main drivers (temperature, dissolved inorganic carbon, alkalinity). We detected carbon biomes by using an agglomerative hierarchical clustering (HC) methodology applied to spatial target-driver relationships, whereby a novel adaptive approach to cut the HC dendrogram based on the compactness and similarity of the clusters was employed. Based only on the spatial variability of the target-driver relationships and with no prior knowledge on the cluster location, we were able to detect well-defined and geographically meaningful carbon biomes. A deep learning model was constructed to track the seasonal and interannual evolution of the carbon biomes, wherein a feed-forward neural network was trained to assign labels to detected biomes. We find that the area covered by the carbon biomes responds robustly to seasonal variations in environmental conditions. A seasonal alternation between different biomes is observed over the North Atlantic and Southern Ocean. Long-term trends in biome coverage over the 1958–2018 period, namely a 10 % expansion of the subtropical biome in the North Atlantic and a 10 % expansion of the subpolar biome in the Southern Ocean, are suggestive of long-term climate shifts. Our approach thus provides a framework that can facilitate the monitoring of the impacts of climate change on the ocean carbon cycle and the evaluation of carbon cycle projections across Earth System Models.

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Sweety Mohanty, Lavinia Patara, Daniyal Kazempour, and Peer Kröger

Status: open (until 18 Jul 2024)

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Sweety Mohanty, Lavinia Patara, Daniyal Kazempour, and Peer Kröger
Sweety Mohanty, Lavinia Patara, Daniyal Kazempour, and Peer Kröger


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Short summary
Climate change vastly affects the ocean carbon cycle, demanding methods to assess and monitor ocean carbon uptake. In this study, we devised a machine learning tool to detect and track ocean carbon biomes from 1958 to 2018. These biomes show consistent relationships between surface CO2 fugacity and its drivers. Using ML methods, we identified and monitored carbon biomes over time, displaying meaningful responses to seasonal and long-term shifts and providing insights into climate change impacts.